Reference evapotranspiration prediction using high-order response surface method

The precision of reference evapotranspiration (ET_o) predictions would vary, depending on the adopted empirical method and the availability of meteorological data. This study aims to enhance the prediction accuracy of ET_o using the high-order response surface method (HO-RSM). Daily scale climatological information are used to build the predictive model including maximum temperature (T_max), maximum humidity (H_max), wind speed (WS), solar radiation (SR), and vapor pressure deficit (VPD), which are obtained from three observation stations in Burkina Faso, West Africa. Ten models corresponding to ten different input combination sets are evaluated for variability influence by comparing the predicted ET_o with the observed ET_o. The models presented a similar performance at both Gaoua and Boromo stations with the determination coefficient (R²) and root mean square error (RMSE) values ranging between 0.6831–0.9966 (0.0622–0.5065) and 0.7237–0.9948 (0.0722–0.4942), respectively. As for the Dori station, the models showed a lower performance with R² (RMSE) values ranging between 0.2068 and 0.5229 (0.8292–1.0051), which may be due to the insufficient input variables or the requirement of higher order in RSM modeling for this station. Results also showed that the M₁₀ model that includes all five input variables performed the best at three stations, with respect to the statistical performance. This is followed by the M₇ model, which excluded the H_max in the prediction, suggesting that H_max has the least influence on the ET_o prediction among all the input variables. The insignificant trend in selecting the optimum order of the RSM also showed that HO-RSM is case sensitive and hence precautions are required for generalizing model applications.